Towards a next-generation computational neuroscience

What don't we know? Recently, the journal Science selected 25 of the biggest unanswered questions facing scientists over the next 25 years. Number two on the list, right after figuring out the composition of the universe, is: What is the biological basis of consciousness? This indeed is a big question. Scientific descriptions of conscious experience, volition, and subjectivity will follow in the footsteps of Copernicus and Darwin by restructuring our relationship with each other and with nature, and many clinical and technological applications will follow.A scientific account of consciousness will not arrive fully formed in a 'Eureka' moment. What is needed is a multidisciplinary, integrative approach combining theory and experiment and exploiting the interchange between the information/computation sciences and the neural, psychological, and medical sciences. At the front-line of this interchange, computational neuroscience (CN) uses computational approaches to model intricate brain processes in much the same way that meteorology uses computers to forecast the weather. In this view and in contrast to early approaches to 'artificial intelligence' (AI), brains are not computers, and intelligent behavior and conscious experience arise from complex brain-body-environment interactions unfolding in temporally precise ways. Much current CN focuses on single levels of description of neural systems (e.g., how neural activity affects connections among neurons) and neglects the multi-scale relations that connect brains, bodies, and behavior. Moreover, current CN is also surprisingly silent with regard to consciousness itself. By targeting and overcoming these limitations, our research will deliver new insights into the neural mechanisms underlying adaptive behavior and conscious experience. We will follow three interacting themes: (i) design and analysis of large-scale CN models to explore how multi-scale neural interactions shape and are shaped by brain-body-environment interactions; (ii) development of new theory to identify causal interactions in complex networks (what we call 'causal network analysis'), and (iii) creation of CN models that account for functionally significant aspects of consciousness, for example that each conscious experience integrates diverse information sources into unified scenes. Theoretical work in the above themes will interact with experimental data from multiple sources. At a fine-grained level we will characterize causal interactions in the intact brain of a pond snail, shedding light on the integrated neural function of a simple (non-conscious) organism as it interacts with its environment. Zooming out, we will apply causal network analysis to brain-imaging data acquired from humans in various states of consciousness, to test predictions based on CN models, and to guide the design of new models. Insights at the fine-grained level will scaffold our understanding of the more complex mechanisms underlying consciousness, with causal networks cross-cutting brains, bodies and environments providing a common theoretical framework. Taken together, these research strands will catalyze an important shift from correlation to explanation in consciousness science.As well as advances in basic science, our research will have important practical benefits at the interface of the biological and information sciences. These will include new design principles for AI/robotic devices, new insights for the design and control of complex technological networks, and new tools for the management of large-scale datasets. A next-generation CN will also underpin new clinical approaches. Many brain-related health problems, from coma to depression to insomnia, can be understood as expressions of disordered consciousness, and many existing clinical approaches are palliative and lacking in theoretical foundation. Our research will provide a theoretical basis for a new generation of effective clinical interventions.